Modelling simultaneous chain‐end and random scissions using the fixed pivot technique
Bibliographic record
Abstract
Abstract In this study, for the first time we demonstrated that both random and chain‐end scissions of polymers can be simulated on a unified Fixed Pivot (FP) framework through an elegant implementation of a discrete‐continuous meshing strategy. Achieved using only a fraction of computational expense in solving the full set of exact equations, the FP solutions benchmarked very well against the exact solutions for a polymer with a broad size distribution typical of natural polymers at different degrees of up to ∼O(105). This is attained despite the use of an efficient computational technique to obtain the exact solutions. Moreover, new observations revealed an additional strength of the current meshing strategy, in that the number of the discrete partitions can be adjusted to improve the accuracy of the solution while retaining the total number of equations to be solved. The FP technique, which in the past was reported to over‐predict in cases of pure aggregation, also exhibits marginal over‐prediction for pure random scission. The source of this behaviour is further uncovered, leading to a revised guideline on the choice of the number of discrete pivots.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".